Machine Learning Engineer

Key Responsibilities

• Develop state-of-the-art reinforcement learning algorithms for robotic control, planning, and decision-making.
• Build and maintain simulation environments (e.g., Isaac Gym, Mujoco, Gazebo, PyBullet) for scalable RL training.
• Train RL agents using deep learning frameworks (PyTorch / TensorFlow) and optimize them for sim-to-real transfer.
• Collaborate with robotics and controls engineers to integrate RL policies on real robotic hardware (arm manipulators, humanoids, etc.)
• Implement robust RL pipelines including data collection, reward shaping, distributed training, and evaluation.
• Perform benchmarking, diagnostics, and troubleshooting of RL algorithms under various operational conditions.
• Research and apply methods such as offline RL, hierarchical RL, imitation learning, model-based RL , or multi-agent RL .
• Optimize for safety, performance, and real-time constraints in robotic applications.
• Document technical findings, results, and best practices.

Required Qualifications